Data Analysis: World Corona Vaccine Progress part 1

Since the inception of this pandemic I have been following it closely, now that we started vaccinating my interest has piqued.

I decided to visit Kaggle and check how are we moving with the vaccination process.

Downloaded the Dataset from Kaggle, https://www.kaggle.com/gpreda/covid-world-vaccination-progress, renamed it to “co.csv” to keep it simple and opened my Jupyter notebook.

import pyforest
df=pd.read_csv("co.csv")

“pyforest” is a library which can be used to import all data science related libraries, saving you a lot of lines of code.

I want to check data only for the latest date, I have used the query to show me only row values which have date= “3/9/2021”.

df2 = df[df['date']== "3/9/2021"]

We have sorted the Dataframe in descending order of “total_vaccinations” column.

df3=df2.sort_values(by=["total_vaccinations"], ascending=False)
print(df3)

USA and India are at the top, Israel is a surprise entry

df3[df3['country']=='Canada']

Checking how many got vaccinated in Canada today, because why not, they are the first G7 country to get Indian vaccines.

Output:

So Canada vaccinated just 69000 people on 9th march.

Checking Indian data.

df3[df3['country']=='India']

Checking which country has highest “people_fully_vaccinated_per_hundred”.

df4=df2.sort_values(by=["people_fully_vaccinated_per_hundred"], ascending=False)

Conclusion:

There is a lot more we can analyze but these are early days, we won’t be able to infer a lot from the open source data. Will wait for the time when the vaccination process will cover half the planet so we can extensively analyze it.

Geopolitics and Data Science enthusiast.

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